Senior Computer Vision Engineer

Senior Computer Vision Engineer

About Us

We are a technology company with roots in algorithmic trading, where we built real-time systems that process massive, noisy data streams with zero tolerance for delay.
We are now applying this engineering DNA to a new MilTech division — an AI battlefield intelligence platform, from petabyte-scale video annotation to real-time inference on custom hardware at command posts.
We have exclusive data partnerships with frontline Ukrainian military units, an operational annotation pipeline with active learning and a trained detection model (v1).
The infrastructure is built and the data is flowing — we need experienced CV engineers to push the models forward.

The Role

We are looking for a Senior CV Engineer to join the ML team and focus on developing, training, and optimizing computer vision and vision-language models for battlefield target detection, classification, and contextual intelligence.

What You Will Do

Model Development & Training
- Train and iterate on detection, classification, tracking, and segmentation models on real combat footage
- Experiment with vision-language models (VLMs) for scene understanding and contextual reasoning
- Run distributed multi-GPU training experiments and manage the experiment lifecycle
- Improve model accuracy through architecture selection, hyperparameter tuning, data augmentation, and training strategy
- Contribute to the active learning loop: analyze confidence distributions, refine auto-labeling thresholds, identify hard cases for human review

Battlefield Contextual Intelligence
- Develop area-level temporal analysis: change detection across sequential overflights, environmental cue recognition
- Build sequence-aware model architectures (temporal attention, video transformers) for multi-frame reasoning
- Collaborate with military subject-matter experts to encode operator knowledge into model features and evaluation criteria

Model Optimization & Deployment
- Quantize and optimize models for deployment on custom AIPU inference hardware (INT8, mixed-precision, quantization-aware training)
- Validate that optimized models meet accuracy targets against gold-set benchmarks
- Support multi-stream inference validation at command post scale (100+ concurrent streams per rack)

Data & Annotation Quality
- Work with the annotation team to refine the labeling ontology and quality control criteria
- Analyze model errors to identify gaps in training data and drive targeted data collection
- Evaluate dataset diversity and coverage using embedding-based curation tools

What We Are Looking For

Must Have
- 4+ years in computer vision and deep learning, with production model deployments
- Strong PyTorch proficiency, including distributed training (DDP/FSDP)
- Hands-on experience with object detection pipelines (YOLO family, RT-DETR, or similar)
- Model optimization experience: quantization, pruning, or export to inference runtimes (ONNX, TensorRT, or similar)
- Solid understanding of training methodology: experiment design, ablation studies, metrics analysis
- Python as primary language; clean, production-grade code

Strong Plus
- Experience with vision-language models (CLIP, Florence, PaliGemma, or similar)
- Video understanding or temporal model architectures
- UAV / drone / robotics domain experience
- MilTech or defense-related work
- Change detection or remote sensing experience
- Experience with active learning or data-centric ML workflows
- C++ for performance-critical components

Tech Stack

- Training: PyTorch, distributed training (DDP/FSDP)
- Data processing: Ray Data
- Data curation: FiftyOne, embedding-based selection
- Annotation: CVAT with pre-annotation and active learning
- Experiment tracking: MLflow
- Inference: Custom AIPU hardware (model export via ONNX + quantization)
- Infrastructure: Kubernetes, on-prem GPU cluster (NVIDIA L40S)
- Storage: S3-compatible object storage (Ceph)

What We Offer

- A role focused on what you do best — building and improving CV models, not setting up infrastructure
- Real combat footage dataset — petabyte-scale, operationally relevant, impossible to replicate
- Dedicated on-prem GPU resources — no cloud queues or budget negotiations for compute
- Direct collaboration with military operators whose domain expertise shapes the models
- Competitive salary
- Stock options